I'm currently using android devices to develop an application that uses sensor values to get information about the environment. I'm using a Samsung S6 Edge and a Nexus 7 tablet and the sensor values i get from the devices are very different.
One example:
These are the Gyroscope values (x-axis) from both devices sitting still on a table. This is one of the best in values similarity, most of them are not even close.
A couple of the bad value similarities:
Magnetic Field:
Accelerometer:
I'm wondering why values are so different across devices?
Thanks.
I'm wondering why values are so different across devices?
They are not particularly different. Those are values in radians/second, where a "360-degree turn" is ~6.28 radians. Your values range from ~-0.002 to +0.004. ~0.004 radians/s is ~0.229 degrees/s. This is tiny. IOW, your values are all similar to 0.
Plus, as the documentation states:
In practice, the gyroscope noise and offset will introduce some errors which need to be compensated for. This is usually done using the information from other sensors, but is beyond the scope of this document.
More generally, different hardware will have different sensors from different manufacturers.
With respect to accelerometer, again, your values are tiny. Earth's gravity is 9.8 m/s2; your values are between ~0.15 and ~-0.05.
With respect to magnetic field... your Samsung seems messed up.
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I am building an Android app (for version 8 currently) that uses the magnetic compass. In the application, I have added a check to confirm that the compass is currently giving accurate results and, if it is not, the user gets prompted to perform a 'figure of 8' calibration sequence.
In order to properly test this, I would like to UNcalibrate the compass, so that I can check that the calibration code is actually working correctly. Is there any way to do this reliably?
Related to compass calibration although that refers to a long superseded version of Android.
If you allow the device to sit motionless for a while (with your app not reading the compass), that may do it.
You also might bring something magnetic close to it to throw the magnetometer off.
My understanding of "calibration" is as follows: the hardware (or low level drivers) know that earth's magnetic field is uniform, hence length of resulting magnetic vector should be constant regardless of device orientation. If there is a second magnetic field which rotates with the device, the sum of the two fields will not be constant; they detect this and mathematically subtract out the second field by analyzing how the sum field is distorted. Waving in a figure 8 provides enough sample data at various orientations to accomplish this.
I am needing to implement a shake recognizer, and I am using the accelerometer on the device to that. However, when I check the values I get from the sensor, it appears that they vary wildly from device to device. For instance, I get a value range of 0-8 as force (after some calculations) on one device, and on the other 0 - 4.
So it looks like they have very different ranges.
Is there anything I can do to make these ranges equal. Or are there some variables that I can use to somehow calculate what a fairly hard shake would be?
According to specification accelerometer should return Measures the acceleration force in m/s2. So it should be calibrated. One thing you could check however is the Sensor class's getMaximumRange() and getResolution()
The physical placement of the chip on the pcb and the securing of the pcb within the device and the construction of the device could all lead to different damping effects in responce to your shaking input force.
You don't say how your processing the sensor data there may well be effects related to sampleing and filtering performed at the driver level.
You clearly need to be flexible in your code with the range of values you expect and test on a good range of devices.
The sensor should be calibrated.
Apparently it isn't. If the gain in the different directions (that is x, y, z) is not significantly different then it is enough to look for sudden changes in the length^2 of the accelerometer vector: x^2+y^2+z^2.
If the gains are also significantly different then you have no choice but to write an app for accelerometer calibration...
By the way, you are not the first one to report gross inaccuracies, see for example Android: the range of z-value in the accelerometer sensor are different on different devices.
I have observed the accelerometer data in several android devices are different in same place with same orientation.
I am developing a game based on accelerometer X and Y data and it is working fine with tablet, but unusual result in android phones.
Please suggest to overcome this issue.
Thanks,
Biplab
This is natural because every device has different hardware specifications, that means that they also have different accelerometer implementations each having different noises in their readings. Even the time bettween two consecutive polls intitialized with the same sensor delay flag (such as SENSOR_DELAY_GAME) will probably differ in different devices. I suggest you apply filtering in your data for smoother readings.
See here for low pass filter.
See here for high pass filter.
The examples are not in java but they will give you the idea.
I am trying to do an analysis which involves interpreting the results from the various sensors of the Android device. Right now I'm analyzing the magnetic field sensor which should tell me (according to this documentation page) the values of the ambient magnetic field in uT (micro Tesla). Everything is clear so far but on the 2 devices that I tested on (Galaxy S, Galaxy Gio) the results are different. And by different I mean very different.
For example having the same orientation on a table here's what those devices show (just one example):
S2: -2,12, 60 (approximate values on x,y,z accordingly)
Gio: -2,12,-36 (approximate values on x,y,z accordingly)
Even if I switch positions the results are the same. I also read on this page about the intensity of the magnetic field and I believe that I should have around 50uT given my geographical latitude.
Can someone explain what those sensors mean?
Does someone know how to explain this behavior?
Thank you,
Iulian
I just can confirm the problem.
As the author of the steady compass application, I have done many tests of magnetic field sensors with different devices. I have found that one of the devices reported an absolute value of mf about 2x the amount reported by other different device under the same conditions.
I have done a lot of test after phone calibration, and I even put the devices in "airplane mode" trying to minimize electromagnetic interferences coming from the own device. Another comment is that plugging the USB cable on some devices can create important magnetic field variations.
The worst results that I have seen on a given device (after calibration, airplane mode and USB disconnected) is this: Using a compass application with the device on horizontal plane, I take a reading, rotate phone 90 deg in the horizontal plane and take a new compass reading. Both readings differ in more than 15 degrees!
Fortunately, not all devices include such a bad sensors. Best devices have errors of about 2-3 degrees after a 90 degrees rotation.
The conclusion: Unless you are very confident on your magnetic field sensor because you have tested and certified a good behavior, you should give a limited reliability to the readings reported by such sensor.
I have also experienced these problems with the sensors. I believe the phones will give different sensor readings based on the hardware used by the manufacturer.
Try reading the accuracy of the sensor for SENSOR_STATUS_UNRELIABLE. You may have to recalibrate the magnetometer.
Reading here: Android Sensors - Which of them get direct input? ,
I am wondering if anyone has experience or a technically detailed link about the accuracy of the linear acceleration versus just manual processing of the acceleration raw data. E.g., do the new phones have dedicated hardware chips for filtering out gravity, or are most devices just going to filter the same raw source?
Update, proposed answer for someone to confirm if they have such a device (Xoom,Nexus S,?):
"If the device has gyro, or possibly multiple accelerometers, then the returned values for gravity (G) versus external linear acceleration (L) can be fundamentally more accurate than any processing on accelerometer data alone. Without extra sensors, e.g. as on most phones, one could in principle post-process the Acceleration (A) to attempt separation as accurately as what the device is returning for A = G+L"
It seems, the gravity/lin. acceleration can be calculated by a low-pass-filter - just as described in the Android-Documentation.
However only filtering the last value will not do it. I get acceptable results by averaging the accelerometer values of ca. 200ms (for moderate movement, this will still screw up, e.g. when you flip your phone fast between your fingers).
Your proposed answer is most likely correct.
You can check the statistics of several smartphone models on Android fragmentation.
For many models the power consumption of the lin. acceleration and gravity sensor is just the sum of accelerometer, gyroscope and magnetometer.
The gyroscope lets you recognize fast angular movement and it can be used to improve the gravity value, which is not possible with just the low-pass-filter. For the magnetometer im not sure if it really gets you more information.
On my phone (HTC One S) the gravity sensor uses just as much power as the accelerometer, but is still better than my simple filter. So either it is another hardware sensor or probably they use different weights on it. I tried to weight acc-data stronger, if their absolute value is closer to gravity, which is nice but was still not as good as the actual gravity sensor.
For compatibility reasons I would suggest to use a low-pass-filter for gravity if possible, as still not every smartphone has a gyroscope or mentioned sensors.